Delete app.py

app.py

@@ -1,97 +0,0 @@
-import gradio as gr
-from matplotlib import gridspec
-import matplotlib.pyplot as plt
-import numpy as np
-from PIL import Image
-import torch
-from transformers import AutoImageProcessor, AutoModelForSemanticSegmentation
-
-MODEL_ID = "nvidia/segformer-b4-finetuned-cityscapes-1024-1024"
-processor = AutoImageProcessor.from_pretrained(MODEL_ID)
-model = AutoModelForSemanticSegmentation.from_pretrained(MODEL_ID)
-
-def ade_palette():
-    """ADE20K palette that maps each class to RGB values."""
-    return [
-        [204, 27, 92], [112, 185, 212], [45, 189, 106], [234, 123, 67], [78, 56, 123], [210, 32, 89],
-        [90, 180, 56], [155, 102, 200], [33, 147, 176], [255, 183, 76], [67, 123, 89], [190, 190, 0],
-        [134, 112, 200], [56, 45, 189], [200, 56, 123], [87, 92, 204], [120, 56, 123], [45, 78, 123],
-        [156, 200, 56],
-    ]
-
-labels_list = []
-with open("labels.txt", "r", encoding="utf-8") as fp:
-    for line in fp:
-        labels_list.append(line.rstrip("\n"))
-
-colormap = np.asarray(ade_palette(), dtype=np.uint8)
-
-def label_to_color_image(label):
-    if label.ndim != 2:
-        raise ValueError("Expect 2-D input label")
-    if np.max(label) >= len(colormap):
-        raise ValueError("label value too large.")
-    return colormap[label]
-
-def draw_plot(pred_img, seg_np):
-    fig = plt.figure(figsize=(20, 15))
-    grid_spec = gridspec.GridSpec(1, 2, width_ratios=[6, 1])
-
-    plt.subplot(grid_spec[0])
-    plt.imshow(pred_img)
-    plt.axis('off')
-
-    LABEL_NAMES = np.asarray(labels_list)
-    FULL_LABEL_MAP = np.arange(len(LABEL_NAMES)).reshape(len(LABEL_NAMES), 1)
-    FULL_COLOR_MAP = label_to_color_image(FULL_LABEL_MAP)
-
-    unique_labels = np.unique(seg_np.astype("uint8"))
-    ax = plt.subplot(grid_spec[1])
-    plt.imshow(FULL_COLOR_MAP[unique_labels].astype(np.uint8), interpolation="nearest")
-    ax.yaxis.tick_right()
-    plt.yticks(range(len(unique_labels)), LABEL_NAMES[unique_labels])
-    plt.xticks([], [])
-    ax.tick_params(width=0.0, labelsize=25)
-    return fig
-
-def run_inference(input_img):
-    # input: numpy array from gradio -> PIL
-    img = Image.fromarray(input_img.astype(np.uint8)) if isinstance(input_img, np.ndarray) else input_img
-    if img.mode != "RGB":
-        img = img.convert("RGB")
-
-    inputs = processor(images=img, return_tensors="pt")
-    with torch.no_grad():
-        outputs = model(**inputs)
-        logits = outputs.logits  # (1, C, h/4, w/4)
-
-    # resize to original
-    upsampled = torch.nn.functional.interpolate(
-        logits, size=img.size[::-1], mode="bilinear", align_corners=False
-    )
-    seg = upsampled.argmax(dim=1)[0].cpu().numpy().astype(np.uint8)  # (H,W)
-
-    # colorize & overlay
-    color_seg = colormap[seg]                                # (H,W,3)
-    pred_img = (np.array(img) * 0.5 + color_seg * 0.5).astype(np.uint8)
-
-    fig = draw_plot(pred_img, seg)
-    return fig
-
-demo = gr.Interface(
-    fn=run_inference,
-    inputs=gr.Image(type="numpy", label="Input Image"),
-    outputs=gr.Plot(label="Overlay + Legend"),
-    examples=[
-        "person-1.jpg",
-        "person-2.jpg",
-        "person-3.jpg",
-        "person-4.jpg",
-        "person-5.jpg"
-    ],
-    flagging_mode="never",
-    cache_examples=False,
-)
-
-if __name__ == "__main__":
-    demo.launch()